`scraped_items.py`: In-memory vector similarity instead of Azure AI Search

[AB-Law]

File: PluckIt.Processor/agents/tools/scraped_items.py:87

Problem

search_scraped_items loads 200 candidate documents from Cosmos — including their full embedding vectors — and computes cosine similarity in Python:

_CANDIDATE_LIMIT = 200  # how many docs to load for in-memory ranking

async for doc in container.query_items(
    query="... ORDER BY c.scoreSignal DESC OFFSET 0 LIMIT 200"
):
    candidates.append(doc)

for doc in candidates:
    emb = doc.get("embedding") or []
    sim = _cosine_similarity(query_embedding, emb)

This approach has two compounding problems:

1. 200 Cosmos reads per search — each document includes its full embedding vector (~6KB for text-embedding-3-small). That's ~1.2 MB of data transferred and ~2,000 RU per tool call.
2. Candidate selection is by scoreSignal, not semantic relevance — the 200 items sent for ranking are chosen by a scraper quality score, not by closeness to the query. A semantically perfect match ranked 201st is invisible.

Impact

Users	Stylist sessions/month	Scraped-item searches	Monthly Cosmos RU cost
10	300	~300	~600K RU (negligible)
100	3,000	~3,000	~6M RU — requires autoscale
1,000	30,000	~30,000	~60M RU — ~$90/month in Cosmos alone

At 100+ users this becomes the single largest Cosmos cost driver.

Proposed Fix

Move similarity search to Azure AI Search with a vector index on the embedding field. The query becomes a single ANN (approximate nearest-neighbour) search call returning the top-K results directly, with no client-side scoring loop and no full-vector document reads from Cosmos.

This is already the infrastructure direction implied by the Azure stack — Azure AI Search is already provisioned in the Terraform config.

Functionality Impact

Results improve: ANN search considers the full corpus rather than the top-200 by score signal, so relevant items that scored low on the scraper heuristic are no longer invisible. Latency also drops — one search API call vs 200 Cosmos reads.

[AB-Law]

Free tier only